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Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
| Content Provider | MDPI |
|---|---|
| Author | Li, Suli Lee, Hyo Jong |
| Copyright Year | 2022 |
| Description | Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age. In this paper, we propose a novel, effective, attention-based feature decomposition model, the age-invariant features extraction network, which can learn more discriminative feature representations and reduce the disturbance caused by aging. Our method uses an efficient channel attention block-based feature decomposition module to extract age-independent identity features from facial representations. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. In addition, we propose a direct sum loss function to reduce the interference of age-related features. Our method achieves a comparable and stable performance. Experimental results demonstrate superior performance on four benchmarked datasets over the state-of-the-art. We obtain the relative improvements of 0.06%, 0.2%, and 2.2% on the cross-age datasets CACD-VS, AgeDB, and CALFW, respectively, and a relative 0.03% improvement on a general dataset LFW. |
| Starting Page | 4816 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12104816 |
| Journal | Applied Sciences |
| Issue Number | 10 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-10 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Convolutional Neural Networks Cross-age Face Recognition Deep Learning Attention Mechanism |
| Content Type | Text |
| Resource Type | Article |
